Overview

Dataset statistics

Number of variables48
Number of observations2795
Missing cells43145
Missing cells (%)32.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 MiB
Average record size in memory1.3 KiB

Variable types

Numeric18
DateTime3
Text5
Categorical19
Boolean3

Alerts

Emergency Responder Injuries has constant value "0.0"Constant
Other Injuries has constant value "0.0"Constant
Operator Employee Fatalities has constant value "0.0"Constant
Emergency Responder Fatalities has constant value "0.0"Constant
Accident Latitude is highly overall correlated with Accident State and 1 other fieldsHigh correlation
Accident Longitude is highly overall correlated with Accident State and 8 other fieldsHigh correlation
Accident State is highly overall correlated with Accident Latitude and 4 other fieldsHigh correlation
Accident Year is highly overall correlated with Operator Employee Injuries and 3 other fieldsHigh correlation
All Costs is highly overall correlated with All Fatalities and 11 other fieldsHigh correlation
All Fatalities is highly overall correlated with Accident Longitude and 14 other fieldsHigh correlation
All Injuries is highly overall correlated with Accident Longitude and 16 other fieldsHigh correlation
Cause Category is highly overall correlated with Cause Subcategory and 1 other fieldsHigh correlation
Cause Subcategory is highly overall correlated with Cause Category and 2 other fieldsHigh correlation
Emergency Response Costs is highly overall correlated with All Costs and 8 other fieldsHigh correlation
Environmental Remediation Costs is highly overall correlated with All Costs and 9 other fieldsHigh correlation
Intentional Release (Barrels) is highly overall correlated with All Fatalities and 7 other fieldsHigh correlation
Liquid Recovery (Barrels) is highly overall correlated with All Fatalities and 8 other fieldsHigh correlation
Liquid Subtype is highly overall correlated with All Costs and 5 other fieldsHigh correlation
Liquid Type is highly overall correlated with All Fatalities and 2 other fieldsHigh correlation
Lost Commodity Costs is highly overall correlated with All Injuries and 5 other fieldsHigh correlation
Net Loss (Barrels) is highly overall correlated with Other FatalitiesHigh correlation
Operator Contractor Fatalities is highly overall correlated with Accident Longitude and 12 other fieldsHigh correlation
Operator Contractor Injuries is highly overall correlated with Accident Longitude and 15 other fieldsHigh correlation
Operator Employee Injuries is highly overall correlated with Accident Longitude and 13 other fieldsHigh correlation
Other Costs is highly overall correlated with All Fatalities and 7 other fieldsHigh correlation
Other Fatalities is highly overall correlated with Accident Longitude and 20 other fieldsHigh correlation
Pipeline Location is highly overall correlated with All Fatalities and 9 other fieldsHigh correlation
Pipeline Shutdown is highly overall correlated with Operator Contractor InjuriesHigh correlation
Pipeline Type is highly overall correlated with Pipeline LocationHigh correlation
Property Damage Costs is highly overall correlated with All Costs and 4 other fieldsHigh correlation
Public Evacuations is highly overall correlated with All Fatalities and 7 other fieldsHigh correlation
Public Fatalities is highly overall correlated with Accident Latitude and 13 other fieldsHigh correlation
Public Injuries is highly overall correlated with Accident Longitude and 16 other fieldsHigh correlation
Public/Private Property Damage Costs is highly overall correlated with All Fatalities and 8 other fieldsHigh correlation
Report Number is highly overall correlated with Accident Year and 2 other fieldsHigh correlation
Supplemental Number is highly overall correlated with Accident Year and 1 other fieldsHigh correlation
Unintentional Release (Barrels) is highly overall correlated with Liquid Recovery (Barrels) and 2 other fieldsHigh correlation
Pipeline Location is highly imbalanced (94.4%)Imbalance
Liquid Ignition is highly imbalanced (78.6%)Imbalance
Liquid Explosion is highly imbalanced (95.2%)Imbalance
Pipeline/Facility Name has 121 (4.3%) missing valuesMissing
Liquid Subtype has 1446 (51.7%) missing valuesMissing
Liquid Name has 2573 (92.1%) missing valuesMissing
Accident City has 315 (11.3%) missing valuesMissing
Accident County has 75 (2.7%) missing valuesMissing
Intentional Release (Barrels) has 1586 (56.7%) missing valuesMissing
Pipeline Shutdown has 212 (7.6%) missing valuesMissing
Shutdown Date/Time has 1405 (50.3%) missing valuesMissing
Restart Date/Time has 1454 (52.0%) missing valuesMissing
Public Evacuations has 457 (16.4%) missing valuesMissing
Operator Employee Injuries has 2783 (99.6%) missing valuesMissing
Operator Contractor Injuries has 2783 (99.6%) missing valuesMissing
Emergency Responder Injuries has 2783 (99.6%) missing valuesMissing
Other Injuries has 2783 (99.6%) missing valuesMissing
Public Injuries has 2783 (99.6%) missing valuesMissing
All Injuries has 2783 (99.6%) missing valuesMissing
Operator Employee Fatalities has 2787 (99.7%) missing valuesMissing
Operator Contractor Fatalities has 2787 (99.7%) missing valuesMissing
Emergency Responder Fatalities has 2787 (99.7%) missing valuesMissing
Other Fatalities has 2787 (99.7%) missing valuesMissing
Public Fatalities has 2787 (99.7%) missing valuesMissing
All Fatalities has 2787 (99.7%) missing valuesMissing
Public Evacuations is highly skewed (γ1 = 32.23809129)Skewed
Property Damage Costs is highly skewed (γ1 = 21.92821158)Skewed
Public/Private Property Damage Costs is highly skewed (γ1 = 35.94608265)Skewed
Emergency Response Costs is highly skewed (γ1 = 28.4186065)Skewed
Environmental Remediation Costs is highly skewed (γ1 = 52.23717109)Skewed
Other Costs is highly skewed (γ1 = 29.87226224)Skewed
All Costs is highly skewed (γ1 = 47.05731007)Skewed
Report Number has unique valuesUnique
Supplemental Number has unique valuesUnique
Unintentional Release (Barrels) has 30 (1.1%) zerosZeros
Intentional Release (Barrels) has 1049 (37.5%) zerosZeros
Liquid Recovery (Barrels) has 738 (26.4%) zerosZeros
Net Loss (Barrels) has 1489 (53.3%) zerosZeros
Public Evacuations has 2285 (81.8%) zerosZeros
Property Damage Costs has 539 (19.3%) zerosZeros
Lost Commodity Costs has 466 (16.7%) zerosZeros
Public/Private Property Damage Costs has 2527 (90.4%) zerosZeros
Emergency Response Costs has 809 (28.9%) zerosZeros
Environmental Remediation Costs has 1240 (44.4%) zerosZeros
Other Costs has 2465 (88.2%) zerosZeros
All Costs has 34 (1.2%) zerosZeros

Reproduction

Analysis started2026-01-19 18:32:55.858504
Analysis finished2026-01-19 18:33:17.753626
Duration21.9 seconds
Software versionydata-profiling v4.18.0
Download configurationconfig.json

Variables

Report Number
Real number (ℝ)

High correlation  Unique 

Distinct2795
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20132931
Minimum20100001
Maximum20170028
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.0 KiB
2026-01-20T00:33:17.810861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20100001
5-th percentile20100146
Q120120036
median20130374
Q320150228
95-th percentile20160319
Maximum20170028
Range70027
Interquartile range (IQR)30191

Descriptive statistics

Standard deviation19820.979
Coefficient of variation (CV)0.00098450539
Kurtosis-1.1579889
Mean20132931
Median Absolute Deviation (MAD)19820
Skewness-0.14470119
Sum5.6271543 × 1010
Variance3.9287122 × 108
MonotonicityNot monotonic
2026-01-20T00:33:17.881086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201700251
 
< 0.1%
201000161
 
< 0.1%
201002541
 
< 0.1%
201000381
 
< 0.1%
201002601
 
< 0.1%
201000301
 
< 0.1%
201000211
 
< 0.1%
201100361
 
< 0.1%
201002551
 
< 0.1%
201002611
 
< 0.1%
Other values (2785)2785
99.6%
ValueCountFrequency (%)
201000011
< 0.1%
201000021
< 0.1%
201000031
< 0.1%
201000041
< 0.1%
201000051
< 0.1%
201000061
< 0.1%
201000071
< 0.1%
201000081
< 0.1%
201000091
< 0.1%
201000101
< 0.1%
ValueCountFrequency (%)
201700281
< 0.1%
201700271
< 0.1%
201700261
< 0.1%
201700251
< 0.1%
201700241
< 0.1%
201700231
< 0.1%
201700221
< 0.1%
201700211
< 0.1%
201700201
< 0.1%
201700191
< 0.1%

Supplemental Number
Real number (ℝ)

High correlation  Unique 

Distinct2795
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19433.319
Minimum15072
Maximum22049
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.0 KiB
2026-01-20T00:33:17.942856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum15072
5-th percentile16518
Q117978.5
median19502
Q320996.5
95-th percentile21885.6
Maximum22049
Range6977
Interquartile range (IQR)3018

Descriptive statistics

Standard deviation1724.8154
Coefficient of variation (CV)0.088755571
Kurtosis-0.90266904
Mean19433.319
Median Absolute Deviation (MAD)1511
Skewness-0.25080502
Sum54316128
Variance2974988.1
MonotonicityNot monotonic
2026-01-20T00:33:18.007509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
220401
 
< 0.1%
173051
 
< 0.1%
173311
 
< 0.1%
177471
 
< 0.1%
185741
 
< 0.1%
162761
 
< 0.1%
171611
 
< 0.1%
180521
 
< 0.1%
185841
 
< 0.1%
180501
 
< 0.1%
Other values (2785)2785
99.6%
ValueCountFrequency (%)
150721
< 0.1%
151141
< 0.1%
151201
< 0.1%
151271
< 0.1%
151301
< 0.1%
151321
< 0.1%
151461
< 0.1%
151621
< 0.1%
151971
< 0.1%
152051
< 0.1%
ValueCountFrequency (%)
220491
< 0.1%
220481
< 0.1%
220461
< 0.1%
220451
< 0.1%
220441
< 0.1%
220431
< 0.1%
220421
< 0.1%
220411
< 0.1%
220401
< 0.1%
220391
< 0.1%

Accident Year
Real number (ℝ)

High correlation 

Distinct8
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.1878
Minimum2010
Maximum2017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.0 KiB
2026-01-20T00:33:18.058905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile2010
Q12012
median2013
Q32015
95-th percentile2016
Maximum2017
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.972102
Coefficient of variation (CV)0.00097959165
Kurtosis-1.1930679
Mean2013.1878
Median Absolute Deviation (MAD)2
Skewness-0.15105802
Sum5626860
Variance3.8891863
MonotonicityIncreasing
2026-01-20T00:33:18.099294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2015462
16.5%
2014454
16.2%
2016415
14.8%
2013401
14.3%
2012366
13.1%
2010350
12.5%
2011345
12.3%
20172
 
0.1%
ValueCountFrequency (%)
2010350
12.5%
2011345
12.3%
2012366
13.1%
2013401
14.3%
2014454
16.2%
2015462
16.5%
2016415
14.8%
20172
 
0.1%
ValueCountFrequency (%)
20172
 
0.1%
2016415
14.8%
2015462
16.5%
2014454
16.2%
2013401
14.3%
2012366
13.1%
2011345
12.3%
2010350
12.5%
Distinct2777
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size22.0 KiB
Minimum2010-01-01 07:15:00
Maximum2017-01-09 07:40:00
Invalid dates0
Invalid dates (%)0.0%
2026-01-20T00:33:18.164750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:18.237032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Operator ID
Real number (ℝ)

Distinct213
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21388.477
Minimum300
Maximum99043
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.0 KiB
2026-01-20T00:33:18.298913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum300
5-th percentile300
Q111169
median26041
Q331618
95-th percentile32537
Maximum99043
Range98743
Interquartile range (IQR)20449

Descriptive statistics

Standard deviation12430.973
Coefficient of variation (CV)0.58119957
Kurtosis0.82129321
Mean21388.477
Median Absolute Deviation (MAD)6106
Skewness-0.18717547
Sum59780792
Variance1.545291 × 108
MonotonicityNot monotonic
2026-01-20T00:33:18.361897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30829201
 
7.2%
18718180
 
6.4%
300156
 
5.6%
31618155
 
5.5%
22610140
 
5.0%
2552136
 
4.9%
1845115
 
4.1%
31684114
 
4.1%
3214787
 
3.1%
2604187
 
3.1%
Other values (203)1424
50.9%
ValueCountFrequency (%)
300156
5.6%
3953
 
0.1%
5151
 
< 0.1%
8791
 
< 0.1%
9991
 
< 0.1%
124813
 
0.5%
15412
 
0.1%
1845115
4.1%
19605
 
0.2%
21621
 
< 0.1%
ValueCountFrequency (%)
990432
 
0.1%
990312
 
0.1%
395341
 
< 0.1%
395091
 
< 0.1%
395042
 
0.1%
394671
 
< 0.1%
394403
0.1%
393491
 
< 0.1%
393075
0.2%
393021
 
< 0.1%
Distinct229
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size203.7 KiB
2026-01-20T00:33:18.486106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length53
Median length45
Mean length25.587478
Min length8

Characters and Unicode

Total characters71517
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique72 ?
Unique (%)2.6%

Sample

1st rowONEOK NGL PIPELINE LP
2nd rowPORTLAND PIPELINE CORP
3rd rowPETROLOGISTICS OLEFINS, LLC
4th rowENBRIDGE ENERGY, LIMITED PARTNERSHIP
5th rowPLAINS PIPELINE, L.P.
ValueCountFrequency (%)
pipeline1637
 
15.7%
llc924
 
8.9%
l.p626
 
6.0%
co519
 
5.0%
lp387
 
3.7%
enterprise358
 
3.4%
company323
 
3.1%
operating229
 
2.2%
crude222
 
2.1%
magellan207
 
2.0%
Other values (286)4978
47.8%
2026-01-20T00:33:18.672761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E8045
11.2%
7640
10.7%
L7104
9.9%
P6990
9.8%
I6693
9.4%
N5653
 
7.9%
O3625
 
5.1%
R3586
 
5.0%
C3347
 
4.7%
A3014
 
4.2%
Other values (26)15820
22.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)71517
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E8045
11.2%
7640
10.7%
L7104
9.9%
P6990
9.8%
I6693
9.4%
N5653
 
7.9%
O3625
 
5.1%
R3586
 
5.0%
C3347
 
4.7%
A3014
 
4.2%
Other values (26)15820
22.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)71517
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E8045
11.2%
7640
10.7%
L7104
9.9%
P6990
9.8%
I6693
9.4%
N5653
 
7.9%
O3625
 
5.1%
R3586
 
5.0%
C3347
 
4.7%
A3014
 
4.2%
Other values (26)15820
22.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)71517
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E8045
11.2%
7640
10.7%
L7104
9.9%
P6990
9.8%
I6693
9.4%
N5653
 
7.9%
O3625
 
5.1%
R3586
 
5.0%
C3347
 
4.7%
A3014
 
4.2%
Other values (26)15820
22.1%
Distinct1977
Distinct (%)73.9%
Missing121
Missing (%)4.3%
Memory size183.2 KiB
2026-01-20T00:33:18.803398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length50
Median length42
Mean length19.640613
Min length2

Characters and Unicode

Total characters52519
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1651 ?
Unique (%)61.7%

Sample

1st rowKINDER MORGAN JCT
2nd row24-INCH MAIN LINE
3rd rowSUPERIOR TERMINAL
4th rowRED RIVER EAST
5th rowHULL STATION
ValueCountFrequency (%)
station928
 
11.5%
terminal343
 
4.3%
to237
 
2.9%
pump215
 
2.7%
pipeline207
 
2.6%
line177
 
2.2%
tank140
 
1.7%
farm120
 
1.5%
108
 
1.3%
city87
 
1.1%
Other values (1834)5496
68.2%
2026-01-20T00:33:19.003609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5534
 
10.5%
T4686
 
8.9%
N4354
 
8.3%
A4236
 
8.1%
E4085
 
7.8%
I3893
 
7.4%
O3617
 
6.9%
S2703
 
5.1%
L2680
 
5.1%
R2553
 
4.9%
Other values (41)14178
27.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)52519
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5534
 
10.5%
T4686
 
8.9%
N4354
 
8.3%
A4236
 
8.1%
E4085
 
7.8%
I3893
 
7.4%
O3617
 
6.9%
S2703
 
5.1%
L2680
 
5.1%
R2553
 
4.9%
Other values (41)14178
27.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)52519
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5534
 
10.5%
T4686
 
8.9%
N4354
 
8.3%
A4236
 
8.1%
E4085
 
7.8%
I3893
 
7.4%
O3617
 
6.9%
S2703
 
5.1%
L2680
 
5.1%
R2553
 
4.9%
Other values (41)14178
27.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)52519
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5534
 
10.5%
T4686
 
8.9%
N4354
 
8.3%
A4236
 
8.1%
E4085
 
7.8%
I3893
 
7.4%
O3617
 
6.9%
S2703
 
5.1%
L2680
 
5.1%
R2553
 
4.9%
Other values (41)14178
27.0%

Pipeline Location
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size153.0 KiB
ONSHORE
2777 
OFFSHORE
 
18

Length

Max length8
Median length7
Mean length7.0064401
Min length7

Characters and Unicode

Total characters19583
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowONSHORE
2nd rowONSHORE
3rd rowONSHORE
4th rowONSHORE
5th rowONSHORE

Common Values

ValueCountFrequency (%)
ONSHORE2777
99.4%
OFFSHORE18
 
0.6%

Length

2026-01-20T00:33:19.056622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-20T00:33:19.101017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
onshore2777
99.4%
offshore18
 
0.6%

Most occurring characters

ValueCountFrequency (%)
O5590
28.5%
S2795
14.3%
H2795
14.3%
E2795
14.3%
R2795
14.3%
N2777
14.2%
F36
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)19583
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O5590
28.5%
S2795
14.3%
H2795
14.3%
E2795
14.3%
R2795
14.3%
N2777
14.2%
F36
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)19583
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O5590
28.5%
S2795
14.3%
H2795
14.3%
E2795
14.3%
R2795
14.3%
N2777
14.2%
F36
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)19583
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O5590
28.5%
S2795
14.3%
H2795
14.3%
E2795
14.3%
R2795
14.3%
N2777
14.2%
F36
 
0.2%

Pipeline Type
Categorical

High correlation 

Distinct4
Distinct (%)0.1%
Missing18
Missing (%)0.6%
Memory size161.8 KiB
ABOVEGROUND
1475 
UNDERGROUND
985 
TANK
301 
TRANSITION AREA
 
16

Length

Max length15
Median length11
Mean length10.264314
Min length4

Characters and Unicode

Total characters28504
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowABOVEGROUND
2nd rowABOVEGROUND
3rd rowABOVEGROUND
4th rowUNDERGROUND
5th rowUNDERGROUND

Common Values

ValueCountFrequency (%)
ABOVEGROUND1475
52.8%
UNDERGROUND985
35.2%
TANK301
 
10.8%
TRANSITION AREA16
 
0.6%
(Missing)18
 
0.6%

Length

2026-01-20T00:33:19.141041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-20T00:33:19.185496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
aboveground1475
52.8%
underground985
35.3%
tank301
 
10.8%
transition16
 
0.6%
area16
 
0.6%

Most occurring characters

ValueCountFrequency (%)
O3951
13.9%
N3778
13.3%
R3477
12.2%
U3445
12.1%
D3445
12.1%
E2476
8.7%
G2460
8.6%
A1824
6.4%
B1475
 
5.2%
V1475
 
5.2%
Other values (5)698
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)28504
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O3951
13.9%
N3778
13.3%
R3477
12.2%
U3445
12.1%
D3445
12.1%
E2476
8.7%
G2460
8.6%
A1824
6.4%
B1475
 
5.2%
V1475
 
5.2%
Other values (5)698
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)28504
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O3951
13.9%
N3778
13.3%
R3477
12.2%
U3445
12.1%
D3445
12.1%
E2476
8.7%
G2460
8.6%
A1824
6.4%
B1475
 
5.2%
V1475
 
5.2%
Other values (5)698
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)28504
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O3951
13.9%
N3778
13.3%
R3477
12.2%
U3445
12.1%
D3445
12.1%
E2476
8.7%
G2460
8.6%
A1824
6.4%
B1475
 
5.2%
V1475
 
5.2%
Other values (5)698
 
2.4%

Liquid Type
Categorical

High correlation 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size210.0 KiB
CRUDE OIL
1398 
REFINED AND/OR PETROLEUM PRODUCT (NON-HVL), LIQUID
939 
HVL OR OTHER FLAMMABLE OR TOXIC FLUID, GAS
418 
CO2 (CARBON DIOXIDE)
 
38
BIOFUEL / ALTERNATIVE FUEL(INCLUDING ETHANOL BLENDS)
 
2

Length

Max length52
Median length9
Mean length27.889803
Min length9

Characters and Unicode

Total characters77952
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHVL OR OTHER FLAMMABLE OR TOXIC FLUID, GAS
2nd rowCRUDE OIL
3rd rowHVL OR OTHER FLAMMABLE OR TOXIC FLUID, GAS
4th rowCRUDE OIL
5th rowCRUDE OIL

Common Values

ValueCountFrequency (%)
CRUDE OIL1398
50.0%
REFINED AND/OR PETROLEUM PRODUCT (NON-HVL), LIQUID939
33.6%
HVL OR OTHER FLAMMABLE OR TOXIC FLUID, GAS418
 
15.0%
CO2 (CARBON DIOXIDE)38
 
1.4%
BIOFUEL / ALTERNATIVE FUEL(INCLUDING ETHANOL BLENDS)2
 
0.1%

Length

2026-01-20T00:33:19.348440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-20T00:33:19.386662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
crude1398
11.7%
oil1398
11.7%
refined939
7.9%
and/or939
7.9%
petroleum939
7.9%
product939
7.9%
non-hvl939
7.9%
liquid939
7.9%
or836
 
7.0%
hvl418
 
3.5%
Other values (14)2216
18.6%

Most occurring characters

ValueCountFrequency (%)
9105
11.7%
O6944
 
8.9%
R6448
 
8.3%
E6040
 
7.7%
L5899
 
7.6%
D5652
 
7.3%
I5135
 
6.6%
U4639
 
6.0%
N3804
 
4.9%
C2833
 
3.6%
Other values (18)21453
27.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)77952
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9105
11.7%
O6944
 
8.9%
R6448
 
8.3%
E6040
 
7.7%
L5899
 
7.6%
D5652
 
7.3%
I5135
 
6.6%
U4639
 
6.0%
N3804
 
4.9%
C2833
 
3.6%
Other values (18)21453
27.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)77952
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9105
11.7%
O6944
 
8.9%
R6448
 
8.3%
E6040
 
7.7%
L5899
 
7.6%
D5652
 
7.3%
I5135
 
6.6%
U4639
 
6.0%
N3804
 
4.9%
C2833
 
3.6%
Other values (18)21453
27.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)77952
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9105
11.7%
O6944
 
8.9%
R6448
 
8.3%
E6040
 
7.7%
L5899
 
7.6%
D5652
 
7.3%
I5135
 
6.6%
U4639
 
6.0%
N3804
 
4.9%
C2833
 
3.6%
Other values (18)21453
27.5%

Liquid Subtype
Categorical

High correlation  Missing 

Distinct8
Distinct (%)0.6%
Missing1446
Missing (%)51.7%
Memory size184.4 KiB
DIESEL, FUEL OIL, KEROSENE, JET FUEL
408 
GASOLINE (NON-ETHANOL)
376 
LPG (LIQUEFIED PETROLEUM GAS) / NGL (NATURAL GAS LIQUID)
188 
OTHER HVL
171 
MIXTURE OF REFINED PRODUCTS (TRANSMIX OR OTHER MIXTURE)
98 
Other values (3)
108 

Length

Max length56
Median length55
Mean length30.85619
Min length5

Characters and Unicode

Total characters41625
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLPG (LIQUEFIED PETROLEUM GAS) / NGL (NATURAL GAS LIQUID)
2nd rowOTHER HVL
3rd rowGASOLINE (NON-ETHANOL)
4th rowLPG (LIQUEFIED PETROLEUM GAS) / NGL (NATURAL GAS LIQUID)
5th rowDIESEL, FUEL OIL, KEROSENE, JET FUEL

Common Values

ValueCountFrequency (%)
DIESEL, FUEL OIL, KEROSENE, JET FUEL408
 
14.6%
GASOLINE (NON-ETHANOL)376
 
13.5%
LPG (LIQUEFIED PETROLEUM GAS) / NGL (NATURAL GAS LIQUID)188
 
6.7%
OTHER HVL171
 
6.1%
MIXTURE OF REFINED PRODUCTS (TRANSMIX OR OTHER MIXTURE)98
 
3.5%
ANHYDROUS AMMONIA55
 
2.0%
OTHER51
 
1.8%
BIODIESEL2
 
0.1%
(Missing)1446
51.7%

Length

2026-01-20T00:33:19.445207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-20T00:33:19.488602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
fuel816
 
13.2%
diesel408
 
6.6%
oil408
 
6.6%
kerosene408
 
6.6%
jet408
 
6.6%
gasoline376
 
6.1%
non-ethanol376
 
6.1%
gas376
 
6.1%
other320
 
5.2%
mixture196
 
3.2%
Other values (16)2089
33.8%

Most occurring characters

ValueCountFrequency (%)
E5484
13.2%
4832
 
11.6%
L3685
 
8.9%
O2858
 
6.9%
N2594
 
6.2%
I2395
 
5.8%
U1917
 
4.6%
T1872
 
4.5%
S1821
 
4.4%
A1767
 
4.2%
Other values (20)12400
29.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)41625
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E5484
13.2%
4832
 
11.6%
L3685
 
8.9%
O2858
 
6.9%
N2594
 
6.2%
I2395
 
5.8%
U1917
 
4.6%
T1872
 
4.5%
S1821
 
4.4%
A1767
 
4.2%
Other values (20)12400
29.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)41625
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E5484
13.2%
4832
 
11.6%
L3685
 
8.9%
O2858
 
6.9%
N2594
 
6.2%
I2395
 
5.8%
U1917
 
4.6%
T1872
 
4.5%
S1821
 
4.4%
A1767
 
4.2%
Other values (20)12400
29.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)41625
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E5484
13.2%
4832
 
11.6%
L3685
 
8.9%
O2858
 
6.9%
N2594
 
6.2%
I2395
 
5.8%
U1917
 
4.6%
T1872
 
4.5%
S1821
 
4.4%
A1767
 
4.2%
Other values (20)12400
29.8%

Liquid Name
Text

Missing 

Distinct69
Distinct (%)31.1%
Missing2573
Missing (%)92.1%
Memory size93.6 KiB
2026-01-20T00:33:19.582373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length40
Median length36
Mean length11.418919
Min length3

Characters and Unicode

Total characters2535
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique51 ?
Unique (%)23.0%

Sample

1st rowETHANE
2nd rowNORMAL BUTANE
3rd rowPROPYLENE
4th rowJET FUEL
5th rowRAW FEED
ValueCountFrequency (%)
propane34
 
9.4%
ethane29
 
8.0%
propylene28
 
7.7%
y-grade24
 
6.6%
ethylene17
 
4.7%
butane17
 
4.7%
gasoline15
 
4.1%
condensate14
 
3.9%
natural14
 
3.9%
mix14
 
3.9%
Other values (80)157
43.3%
2026-01-20T00:33:19.717565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E394
15.5%
A249
 
9.8%
N244
 
9.6%
P175
 
6.9%
R157
 
6.2%
T143
 
5.6%
143
 
5.6%
O141
 
5.6%
L124
 
4.9%
Y86
 
3.4%
Other values (34)679
26.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)2535
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E394
15.5%
A249
 
9.8%
N244
 
9.6%
P175
 
6.9%
R157
 
6.2%
T143
 
5.6%
143
 
5.6%
O141
 
5.6%
L124
 
4.9%
Y86
 
3.4%
Other values (34)679
26.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2535
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E394
15.5%
A249
 
9.8%
N244
 
9.6%
P175
 
6.9%
R157
 
6.2%
T143
 
5.6%
143
 
5.6%
O141
 
5.6%
L124
 
4.9%
Y86
 
3.4%
Other values (34)679
26.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2535
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E394
15.5%
A249
 
9.8%
N244
 
9.6%
P175
 
6.9%
R157
 
6.2%
T143
 
5.6%
143
 
5.6%
O141
 
5.6%
L124
 
4.9%
Y86
 
3.4%
Other values (34)679
26.8%

Accident City
Text

Missing 

Distinct1027
Distinct (%)41.4%
Missing315
Missing (%)11.3%
Memory size148.7 KiB
2026-01-20T00:33:19.840651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length44
Median length30
Mean length8.2866935
Min length3

Characters and Unicode

Total characters20551
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique661 ?
Unique (%)26.7%

Sample

1st rowMCPHERSON
2nd rowRAYMOND
3rd rowSULPHER
4th rowSUPERIOR
5th rowSHERMAN
ValueCountFrequency (%)
city92
 
3.0%
cushing80
 
2.6%
pasadena71
 
2.3%
houston50
 
1.6%
midland45
 
1.5%
port42
 
1.4%
beaumont36
 
1.2%
arthur35
 
1.1%
texas28
 
0.9%
park27
 
0.9%
Other values (1024)2597
83.7%
2026-01-20T00:33:20.022735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A1955
 
9.5%
E1927
 
9.4%
N1693
 
8.2%
O1625
 
7.9%
R1420
 
6.9%
L1314
 
6.4%
I1235
 
6.0%
S1168
 
5.7%
T1148
 
5.6%
C832
 
4.0%
Other values (31)6234
30.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)20551
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A1955
 
9.5%
E1927
 
9.4%
N1693
 
8.2%
O1625
 
7.9%
R1420
 
6.9%
L1314
 
6.4%
I1235
 
6.0%
S1168
 
5.7%
T1148
 
5.6%
C832
 
4.0%
Other values (31)6234
30.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)20551
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A1955
 
9.5%
E1927
 
9.4%
N1693
 
8.2%
O1625
 
7.9%
R1420
 
6.9%
L1314
 
6.4%
I1235
 
6.0%
S1168
 
5.7%
T1148
 
5.6%
C832
 
4.0%
Other values (31)6234
30.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)20551
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A1955
 
9.5%
E1927
 
9.4%
N1693
 
8.2%
O1625
 
7.9%
R1420
 
6.9%
L1314
 
6.4%
I1235
 
6.0%
S1168
 
5.7%
T1148
 
5.6%
C832
 
4.0%
Other values (31)6234
30.3%

Accident County
Text

Missing 

Distinct678
Distinct (%)24.9%
Missing75
Missing (%)2.7%
Memory size152.4 KiB
2026-01-20T00:33:20.167682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length29
Median length24
Mean length7.4580882
Min length2

Characters and Unicode

Total characters20286
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique346 ?
Unique (%)12.7%

Sample

1st rowMCPHERSON
2nd rowCUMBERLAND
3rd rowCALCASIEU
4th rowDOUGLAS
5th rowGRAYSON
ValueCountFrequency (%)
harris170
 
5.4%
jefferson113
 
3.6%
county101
 
3.2%
angeles57
 
1.8%
los57
 
1.8%
payne52
 
1.7%
midland50
 
1.6%
middlesex46
 
1.5%
kern42
 
1.3%
st41
 
1.3%
Other values (601)2419
76.8%
2026-01-20T00:33:20.382046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A2113
 
10.4%
E2027
 
10.0%
N1726
 
8.5%
R1708
 
8.4%
O1477
 
7.3%
S1401
 
6.9%
L1237
 
6.1%
I1110
 
5.5%
T886
 
4.4%
C812
 
4.0%
Other values (31)5789
28.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)20286
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A2113
 
10.4%
E2027
 
10.0%
N1726
 
8.5%
R1708
 
8.4%
O1477
 
7.3%
S1401
 
6.9%
L1237
 
6.1%
I1110
 
5.5%
T886
 
4.4%
C812
 
4.0%
Other values (31)5789
28.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)20286
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A2113
 
10.4%
E2027
 
10.0%
N1726
 
8.5%
R1708
 
8.4%
O1477
 
7.3%
S1401
 
6.9%
L1237
 
6.1%
I1110
 
5.5%
T886
 
4.4%
C812
 
4.0%
Other values (31)5789
28.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)20286
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A2113
 
10.4%
E2027
 
10.0%
N1726
 
8.5%
R1708
 
8.4%
O1477
 
7.3%
S1401
 
6.9%
L1237
 
6.1%
I1110
 
5.5%
T886
 
4.4%
C812
 
4.0%
Other values (31)5789
28.5%

Accident State
Categorical

High correlation 

Distinct46
Distinct (%)1.7%
Missing12
Missing (%)0.4%
Memory size139.4 KiB
TX
1004 
OK
236 
LA
169 
CA
153 
KS
150 
Other values (41)
1071 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters5566
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st rowKS
2nd rowME
3rd rowLA
4th rowWI
5th rowTX

Common Values

ValueCountFrequency (%)
TX1004
35.9%
OK236
 
8.4%
LA169
 
6.0%
CA153
 
5.5%
KS150
 
5.4%
IL108
 
3.9%
WY98
 
3.5%
NJ85
 
3.0%
MN59
 
2.1%
NM57
 
2.0%
Other values (36)664
23.8%

Length

2026-01-20T00:33:20.440054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tx1004
36.1%
ok236
 
8.5%
la169
 
6.1%
ca153
 
5.5%
ks150
 
5.4%
il108
 
3.9%
wy98
 
3.5%
nj85
 
3.1%
mn59
 
2.1%
nm57
 
2.0%
Other values (36)664
23.9%

Most occurring characters

ValueCountFrequency (%)
T1051
18.9%
X1004
18.0%
A505
9.1%
K411
 
7.4%
N366
 
6.6%
O359
 
6.4%
L305
 
5.5%
I274
 
4.9%
M260
 
4.7%
C210
 
3.8%
Other values (13)821
14.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)5566
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T1051
18.9%
X1004
18.0%
A505
9.1%
K411
 
7.4%
N366
 
6.6%
O359
 
6.4%
L305
 
5.5%
I274
 
4.9%
M260
 
4.7%
C210
 
3.8%
Other values (13)821
14.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5566
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T1051
18.9%
X1004
18.0%
A505
9.1%
K411
 
7.4%
N366
 
6.6%
O359
 
6.4%
L305
 
5.5%
I274
 
4.9%
M260
 
4.7%
C210
 
3.8%
Other values (13)821
14.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5566
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T1051
18.9%
X1004
18.0%
A505
9.1%
K411
 
7.4%
N366
 
6.6%
O359
 
6.4%
L305
 
5.5%
I274
 
4.9%
M260
 
4.7%
C210
 
3.8%
Other values (13)821
14.8%

Accident Latitude
Real number (ℝ)

High correlation 

Distinct2552
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.781608
Minimum18.44801
Maximum70.261265
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.0 KiB
2026-01-20T00:33:20.501103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18.44801
5-th percentile29.193408
Q130.909375
median34.92544
Q340.264062
95-th percentile45.886617
Maximum70.261265
Range51.813255
Interquartile range (IQR)9.354687

Descriptive statistics

Standard deviation5.652599
Coefficient of variation (CV)0.15797499
Kurtosis2.9839586
Mean35.781608
Median Absolute Deviation (MAD)4.591596
Skewness1.0577624
Sum100009.59
Variance31.951875
MonotonicityNot monotonic
2026-01-20T00:33:20.565210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.5224332
 
1.1%
29.729797
 
0.3%
32.07117
 
0.3%
29.725366
 
0.2%
35.958856
 
0.2%
29.435736
 
0.2%
46.688895
 
0.2%
30.7139295
 
0.2%
40.5233274
 
0.1%
35.979163234
 
0.1%
Other values (2542)2713
97.1%
ValueCountFrequency (%)
18.448011
< 0.1%
21.304381
< 0.1%
21.316681
< 0.1%
21.317741
< 0.1%
23.3367841
< 0.1%
25.9642381
< 0.1%
26.6504651
< 0.1%
27.55291821
< 0.1%
27.684021
< 0.1%
27.722221
< 0.1%
ValueCountFrequency (%)
70.2612651
< 0.1%
70.2571471
< 0.1%
70.256661
< 0.1%
70.151204541
< 0.1%
70.107621
< 0.1%
67.79872
0.1%
66.8128231
< 0.1%
65.311111
< 0.1%
63.930741
< 0.1%
63.425461
< 0.1%

Accident Longitude
Real number (ℝ)

High correlation 

Distinct2557
Distinct (%)91.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-95.682691
Minimum-158.09993
Maximum104.2634
Zeros0
Zeros (%)0.0%
Negative2792
Negative (%)99.9%
Memory size22.0 KiB
2026-01-20T00:33:20.626923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-158.09993
5-th percentile-118.22202
Q1-100.58764
median-95.48887
Q3-91.089463
95-th percentile-75.421632
Maximum104.2634
Range262.36333
Interquartile range (IQR)9.4981727

Descriptive statistics

Standard deviation12.32843
Coefficient of variation (CV)-0.12884702
Kurtosis65.159769
Mean-95.682691
Median Absolute Deviation (MAD)4.56785
Skewness3.661965
Sum-267433.12
Variance151.99019
MonotonicityNot monotonic
2026-01-20T00:33:20.688385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-74.2538432
 
1.1%
-95.222127
 
0.3%
-96.474097
 
0.3%
-95.1218356
 
0.2%
-95.203346
 
0.2%
-96.756556
 
0.2%
-91.2750485
 
0.2%
-92.059725
 
0.2%
-94.97315754
 
0.1%
-95.495142044
 
0.1%
Other values (2547)2713
97.1%
ValueCountFrequency (%)
-158.099931
< 0.1%
-158.09371
< 0.1%
-157.890551
< 0.1%
-150.6626811
< 0.1%
-149.81642
0.1%
-148.6219731
< 0.1%
-148.62031
< 0.1%
-148.6118441
< 0.1%
-148.2791
< 0.1%
-147.386331
< 0.1%
ValueCountFrequency (%)
104.2633991
< 0.1%
96.866141
< 0.1%
88.1731361
< 0.1%
-14.355831
< 0.1%
-66.074761
< 0.1%
-70.493361
< 0.1%
-72.5806331
< 0.1%
-72.5807441
< 0.1%
-72.89991
< 0.1%
-72.909251
< 0.1%

Cause Category
Categorical

High correlation 

Distinct7
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size191.1 KiB
MATERIAL/WELD/EQUIP FAILURE
1435 
CORROSION
592 
INCORRECT OPERATION
378 
NATURAL FORCE DAMAGE
 
118
ALL OTHER CAUSES
 
118
Other values (2)
154 

Length

Max length27
Median length27
Mean length20.978175
Min length9

Characters and Unicode

Total characters58634
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowINCORRECT OPERATION
2nd rowMATERIAL/WELD/EQUIP FAILURE
3rd rowMATERIAL/WELD/EQUIP FAILURE
4th rowNATURAL FORCE DAMAGE
5th rowEXCAVATION DAMAGE

Common Values

ValueCountFrequency (%)
MATERIAL/WELD/EQUIP FAILURE1435
51.3%
CORROSION592
21.2%
INCORRECT OPERATION378
 
13.5%
NATURAL FORCE DAMAGE118
 
4.2%
ALL OTHER CAUSES118
 
4.2%
EXCAVATION DAMAGE97
 
3.5%
OTHER OUTSIDE FORCE DAMAGE57
 
2.0%

Length

2026-01-20T00:33:20.754878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-20T00:33:20.801451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
material/weld/equip1435
26.8%
failure1435
26.8%
corrosion592
11.1%
incorrect378
 
7.1%
operation378
 
7.1%
damage272
 
5.1%
force175
 
3.3%
other175
 
3.3%
natural118
 
2.2%
all118
 
2.2%
Other values (3)272
 
5.1%

Most occurring characters

ValueCountFrequency (%)
E7390
12.6%
A5893
10.1%
I5807
 
9.9%
R5656
 
9.6%
L4659
 
7.9%
O3414
 
5.8%
U3163
 
5.4%
/2870
 
4.9%
T2638
 
4.5%
2553
 
4.4%
Other values (13)14591
24.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)58634
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E7390
12.6%
A5893
10.1%
I5807
 
9.9%
R5656
 
9.6%
L4659
 
7.9%
O3414
 
5.8%
U3163
 
5.4%
/2870
 
4.9%
T2638
 
4.5%
2553
 
4.4%
Other values (13)14591
24.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)58634
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E7390
12.6%
A5893
10.1%
I5807
 
9.9%
R5656
 
9.6%
L4659
 
7.9%
O3414
 
5.8%
U3163
 
5.4%
/2870
 
4.9%
T2638
 
4.5%
2553
 
4.4%
Other values (13)14591
24.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)58634
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E7390
12.6%
A5893
10.1%
I5807
 
9.9%
R5656
 
9.6%
L4659
 
7.9%
O3414
 
5.8%
U3163
 
5.4%
/2870
 
4.9%
T2638
 
4.5%
2553
 
4.4%
Other values (13)14591
24.9%

Cause Subcategory
Categorical

High correlation 

Distinct38
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size201.0 KiB
INTERNAL
362 
PUMP OR PUMP-RELATED EQUIPMENT
296 
NON-THREADED CONNECTION FAILURE
286 
EXTERNAL
230 
OTHER EQUIPMENT FAILURE
204 
Other values (33)
1417 

Length

Max length49
Median length39
Mean length24.610733
Min length7

Characters and Unicode

Total characters68787
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.1%

Sample

1st rowPIPELINE/EQUIPMENT OVERPRESSURED
2nd rowPUMP OR PUMP-RELATED EQUIPMENT
3rd rowDEFECTIVE OR LOOSE TUBING/FITTING
4th rowTEMPERATURE
5th rowTHIRD PARTY EXCAVATION DAMAGE

Common Values

ValueCountFrequency (%)
INTERNAL362
13.0%
PUMP OR PUMP-RELATED EQUIPMENT296
 
10.6%
NON-THREADED CONNECTION FAILURE286
 
10.2%
EXTERNAL230
 
8.2%
OTHER EQUIPMENT FAILURE204
 
7.3%
MALFUNCTION OF CONTROL/RELIEF EQUIPMENT171
 
6.1%
THREADED CONNECTION/COUPLING FAILURE151
 
5.4%
CONSTRUCTION, INSTALLATION OR FABRICATION-RELATED112
 
4.0%
OTHER INCORRECT OPERATION86
 
3.1%
INCORRECT VALVE POSITION84
 
3.0%
Other values (28)813
29.1%

Length

2026-01-20T00:33:20.870036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
equipment749
 
10.1%
failure708
 
9.5%
or492
 
6.6%
internal362
 
4.9%
other324
 
4.4%
of307
 
4.1%
pump296
 
4.0%
pump-related296
 
4.0%
non-threaded286
 
3.8%
connection286
 
3.8%
Other values (67)3338
44.8%

Most occurring characters

ValueCountFrequency (%)
E8143
11.8%
N6395
 
9.3%
T5586
 
8.1%
O5142
 
7.5%
R5139
 
7.5%
I4958
 
7.2%
4649
 
6.8%
A4384
 
6.4%
L3568
 
5.2%
U3066
 
4.5%
Other values (19)17757
25.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)68787
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E8143
11.8%
N6395
 
9.3%
T5586
 
8.1%
O5142
 
7.5%
R5139
 
7.5%
I4958
 
7.2%
4649
 
6.8%
A4384
 
6.4%
L3568
 
5.2%
U3066
 
4.5%
Other values (19)17757
25.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)68787
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E8143
11.8%
N6395
 
9.3%
T5586
 
8.1%
O5142
 
7.5%
R5139
 
7.5%
I4958
 
7.2%
4649
 
6.8%
A4384
 
6.4%
L3568
 
5.2%
U3066
 
4.5%
Other values (19)17757
25.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)68787
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E8143
11.8%
N6395
 
9.3%
T5586
 
8.1%
O5142
 
7.5%
R5139
 
7.5%
I4958
 
7.2%
4649
 
6.8%
A4384
 
6.4%
L3568
 
5.2%
U3066
 
4.5%
Other values (19)17757
25.8%

Unintentional Release (Barrels)
Real number (ℝ)

High correlation  Zeros 

Distinct697
Distinct (%)24.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean207.24584
Minimum0
Maximum30565
Zeros30
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size22.0 KiB
2026-01-20T00:33:20.927591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q10.48
median2
Q320
95-th percentile600
Maximum30565
Range30565
Interquartile range (IQR)19.52

Descriptive statistics

Standard deviation1368.6678
Coefficient of variation (CV)6.6040785
Kurtosis228.38617
Mean207.24584
Median Absolute Deviation (MAD)1.86
Skewness13.625789
Sum579252.13
Variance1873251.6
MonotonicityNot monotonic
2026-01-20T00:33:20.990626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1170
 
6.1%
2107
 
3.8%
0.2498
 
3.5%
383
 
3.0%
0.275
 
2.7%
0.568
 
2.4%
566
 
2.4%
0.4858
 
2.1%
1056
 
2.0%
454
 
1.9%
Other values (687)1960
70.1%
ValueCountFrequency (%)
030
1.1%
0.0123
0.8%
0.0220
0.7%
0.031
 
< 0.1%
0.042
 
0.1%
0.056
 
0.2%
0.062
 
0.1%
0.074
 
0.1%
0.083
 
0.1%
0.091
 
< 0.1%
ValueCountFrequency (%)
305651
< 0.1%
271231
< 0.1%
237021
< 0.1%
206001
< 0.1%
200821
< 0.1%
184001
< 0.1%
137181
< 0.1%
128361
< 0.1%
122291
< 0.1%
114051
< 0.1%

Intentional Release (Barrels)
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct117
Distinct (%)9.7%
Missing1586
Missing (%)56.7%
Infinite0
Infinite (%)0.0%
Mean238.53362
Minimum0
Maximum70191
Zeros1049
Zeros (%)37.5%
Negative0
Negative (%)0.0%
Memory size22.0 KiB
2026-01-20T00:33:21.056031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile59.6
Maximum70191
Range70191
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2781.7715
Coefficient of variation (CV)11.661968
Kurtosis396.21607
Mean238.53362
Median Absolute Deviation (MAD)0
Skewness18.405707
Sum288387.15
Variance7738252.4
MonotonicityNot monotonic
2026-01-20T00:33:21.120959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01049
37.5%
0.119
 
0.7%
54
 
0.1%
14
 
0.1%
0.23
 
0.1%
203
 
0.1%
0.013
 
0.1%
3.33
 
0.1%
5003
 
0.1%
32
 
0.1%
Other values (107)116
 
4.2%
(Missing)1586
56.7%
ValueCountFrequency (%)
01049
37.5%
0.013
 
0.1%
0.052
 
0.1%
0.119
 
0.7%
0.121
 
< 0.1%
0.23
 
0.1%
0.231
 
< 0.1%
0.241
 
< 0.1%
0.481
 
< 0.1%
0.52
 
0.1%
ValueCountFrequency (%)
701911
< 0.1%
431801
< 0.1%
306991
< 0.1%
215061
< 0.1%
207771
< 0.1%
178251
< 0.1%
135001
< 0.1%
117091
< 0.1%
66921
< 0.1%
49251
< 0.1%

Liquid Recovery (Barrels)
Real number (ℝ)

High correlation  Zeros 

Distinct539
Distinct (%)19.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.051792
Minimum0
Maximum18245
Zeros738
Zeros (%)26.4%
Negative0
Negative (%)0.0%
Memory size22.0 KiB
2026-01-20T00:33:21.184709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.74
Q38
95-th percentile250
Maximum18245
Range18245
Interquartile range (IQR)8

Descriptive statistics

Standard deviation546.18825
Coefficient of variation (CV)7.2774844
Kurtosis520.55071
Mean75.051792
Median Absolute Deviation (MAD)0.74
Skewness19.534404
Sum209769.76
Variance298321.6
MonotonicityNot monotonic
2026-01-20T00:33:21.253804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0738
26.4%
1121
 
4.3%
275
 
2.7%
0.2473
 
2.6%
0.560
 
2.1%
0.259
 
2.1%
1054
 
1.9%
351
 
1.8%
545
 
1.6%
441
 
1.5%
Other values (529)1478
52.9%
ValueCountFrequency (%)
0738
26.4%
0.0116
 
0.6%
0.028
 
0.3%
0.031
 
< 0.1%
0.054
 
0.1%
0.061
 
< 0.1%
0.074
 
0.1%
0.081
 
< 0.1%
0.091
 
< 0.1%
0.124
 
0.9%
ValueCountFrequency (%)
182451
< 0.1%
99501
< 0.1%
75381
< 0.1%
73271
< 0.1%
66431
< 0.1%
58561
< 0.1%
53001
< 0.1%
49781
< 0.1%
43831
< 0.1%
40691
< 0.1%

Net Loss (Barrels)
Real number (ℝ)

High correlation  Zeros 

Distinct443
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132.19405
Minimum0
Maximum30565
Zeros1489
Zeros (%)53.3%
Negative0
Negative (%)0.0%
Memory size22.0 KiB
2026-01-20T00:33:21.323266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile200
Maximum30565
Range30565
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1185.0193
Coefficient of variation (CV)8.9642405
Kurtosis351.57259
Mean132.19405
Median Absolute Deviation (MAD)0
Skewness17.087587
Sum369482.37
Variance1404270.6
MonotonicityNot monotonic
2026-01-20T00:33:21.384739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01489
53.3%
198
 
3.5%
273
 
2.6%
0.149
 
1.8%
0.548
 
1.7%
544
 
1.6%
336
 
1.3%
0.229
 
1.0%
0.2424
 
0.9%
1022
 
0.8%
Other values (433)883
31.6%
ValueCountFrequency (%)
01489
53.3%
0.0111
 
0.4%
0.0218
 
0.6%
0.036
 
0.2%
0.044
 
0.1%
0.0512
 
0.4%
0.063
 
0.1%
0.074
 
0.1%
0.083
 
0.1%
0.096
 
0.2%
ValueCountFrequency (%)
305651
< 0.1%
271231
< 0.1%
237021
< 0.1%
184001
< 0.1%
147441
< 0.1%
137181
< 0.1%
128361
< 0.1%
114051
< 0.1%
80001
< 0.1%
78461
< 0.1%

Liquid Ignition
Boolean

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
False
2700 
True
 
95
ValueCountFrequency (%)
False2700
96.6%
True95
 
3.4%
2026-01-20T00:33:21.425483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Liquid Explosion
Boolean

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
False
2780 
True
 
15
ValueCountFrequency (%)
False2780
99.5%
True15
 
0.5%
2026-01-20T00:33:21.446910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Pipeline Shutdown
Boolean

High correlation  Missing 

Distinct2
Distinct (%)0.1%
Missing212
Missing (%)7.6%
Memory size5.6 KiB
True
1395 
False
1188 
(Missing)
212 
ValueCountFrequency (%)
True1395
49.9%
False1188
42.5%
(Missing)212
 
7.6%
2026-01-20T00:33:21.468470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Shutdown Date/Time
Date

Missing 

Distinct1385
Distinct (%)99.6%
Missing1405
Missing (%)50.3%
Memory size22.0 KiB
Minimum2010-01-08 23:41:00
Maximum2016-12-28 16:20:00
Invalid dates0
Invalid dates (%)0.0%
2026-01-20T00:33:21.513332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:21.580545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Restart Date/Time
Date

Missing 

Distinct1334
Distinct (%)99.5%
Missing1454
Missing (%)52.0%
Memory size22.0 KiB
Minimum2010-01-11 22:08:00
Maximum2017-01-06 17:16:00
Invalid dates0
Invalid dates (%)0.0%
2026-01-20T00:33:21.641087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:21.713289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Public Evacuations
Real number (ℝ)

High correlation  Missing  Skewed  Zeros 

Distinct35
Distinct (%)1.5%
Missing457
Missing (%)16.4%
Infinite0
Infinite (%)0.0%
Mean0.96407186
Minimum0
Maximum700
Zeros2285
Zeros (%)81.8%
Negative0
Negative (%)0.0%
Memory size22.0 KiB
2026-01-20T00:33:21.778129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum700
Range700
Interquartile range (IQR)0

Descriptive statistics

Standard deviation18.151398
Coefficient of variation (CV)18.827848
Kurtosis1134.4098
Mean0.96407186
Median Absolute Deviation (MAD)0
Skewness32.238091
Sum2254
Variance329.47325
MonotonicityNot monotonic
2026-01-20T00:33:21.832614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
02285
81.8%
16
 
0.2%
24
 
0.1%
123
 
0.1%
53
 
0.1%
132
 
0.1%
82
 
0.1%
302
 
0.1%
62
 
0.1%
42
 
0.1%
Other values (25)27
 
1.0%
(Missing)457
 
16.4%
ValueCountFrequency (%)
02285
81.8%
16
 
0.2%
24
 
0.1%
31
 
< 0.1%
42
 
0.1%
53
 
0.1%
62
 
0.1%
72
 
0.1%
82
 
0.1%
91
 
< 0.1%
ValueCountFrequency (%)
7001
< 0.1%
4701
< 0.1%
1501
< 0.1%
831
< 0.1%
751
< 0.1%
701
< 0.1%
611
< 0.1%
601
< 0.1%
471
< 0.1%
401
< 0.1%

Operator Employee Injuries
Categorical

High correlation  Missing 

Distinct2
Distinct (%)16.7%
Missing2783
Missing (%)99.6%
Memory size152.9 KiB
0.0
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters36
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.09
 
0.3%
1.03
 
0.1%
(Missing)2783
99.6%

Length

2026-01-20T00:33:21.891602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-20T00:33:21.923277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.09
75.0%
1.03
 
25.0%

Most occurring characters

ValueCountFrequency (%)
021
58.3%
.12
33.3%
13
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)36
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
021
58.3%
.12
33.3%
13
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)36
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
021
58.3%
.12
33.3%
13
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)36
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
021
58.3%
.12
33.3%
13
 
8.3%

Operator Contractor Injuries
Categorical

High correlation  Missing 

Distinct5
Distinct (%)41.7%
Missing2783
Missing (%)99.6%
Memory size152.9 KiB
0.0
1.0
3.0
2.0
4.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters36
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)25.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.06
 
0.2%
1.03
 
0.1%
3.01
 
< 0.1%
2.01
 
< 0.1%
4.01
 
< 0.1%
(Missing)2783
99.6%

Length

2026-01-20T00:33:21.964812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-20T00:33:22.004002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.06
50.0%
1.03
25.0%
3.01
 
8.3%
2.01
 
8.3%
4.01
 
8.3%

Most occurring characters

ValueCountFrequency (%)
018
50.0%
.12
33.3%
13
 
8.3%
31
 
2.8%
21
 
2.8%
41
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)36
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
018
50.0%
.12
33.3%
13
 
8.3%
31
 
2.8%
21
 
2.8%
41
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)36
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
018
50.0%
.12
33.3%
13
 
8.3%
31
 
2.8%
21
 
2.8%
41
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)36
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
018
50.0%
.12
33.3%
13
 
8.3%
31
 
2.8%
21
 
2.8%
41
 
2.8%

Emergency Responder Injuries
Categorical

Constant  Missing 

Distinct1
Distinct (%)8.3%
Missing2783
Missing (%)99.6%
Memory size152.9 KiB
0.0
12 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters36
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.012
 
0.4%
(Missing)2783
99.6%

Length

2026-01-20T00:33:22.059784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-20T00:33:22.089077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.012
100.0%

Most occurring characters

ValueCountFrequency (%)
024
66.7%
.12
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)36
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
024
66.7%
.12
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)36
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
024
66.7%
.12
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)36
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
024
66.7%
.12
33.3%

Other Injuries
Categorical

Constant  Missing 

Distinct1
Distinct (%)8.3%
Missing2783
Missing (%)99.6%
Memory size152.9 KiB
0.0
12 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters36
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.012
 
0.4%
(Missing)2783
99.6%

Length

2026-01-20T00:33:22.126546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-20T00:33:22.157211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.012
100.0%

Most occurring characters

ValueCountFrequency (%)
024
66.7%
.12
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)36
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
024
66.7%
.12
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)36
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
024
66.7%
.12
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)36
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
024
66.7%
.12
33.3%

Public Injuries
Categorical

High correlation  Missing 

Distinct3
Distinct (%)25.0%
Missing2783
Missing (%)99.6%
Memory size152.9 KiB
0.0
1.0
3.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters36
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)8.3%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.09
 
0.3%
1.02
 
0.1%
3.01
 
< 0.1%
(Missing)2783
99.6%

Length

2026-01-20T00:33:22.202300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-20T00:33:22.242306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.09
75.0%
1.02
 
16.7%
3.01
 
8.3%

Most occurring characters

ValueCountFrequency (%)
021
58.3%
.12
33.3%
12
 
5.6%
31
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)36
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
021
58.3%
.12
33.3%
12
 
5.6%
31
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)36
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
021
58.3%
.12
33.3%
12
 
5.6%
31
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)36
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
021
58.3%
.12
33.3%
12
 
5.6%
31
 
2.8%

All Injuries
Categorical

High correlation  Missing 

Distinct4
Distinct (%)33.3%
Missing2783
Missing (%)99.6%
Memory size152.9 KiB
1.0
3.0
2.0
4.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters36
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)16.7%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.08
 
0.3%
3.02
 
0.1%
2.01
 
< 0.1%
4.01
 
< 0.1%
(Missing)2783
99.6%

Length

2026-01-20T00:33:22.291612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-20T00:33:22.330345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.08
66.7%
3.02
 
16.7%
2.01
 
8.3%
4.01
 
8.3%

Most occurring characters

ValueCountFrequency (%)
.12
33.3%
012
33.3%
18
22.2%
32
 
5.6%
21
 
2.8%
41
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)36
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
.12
33.3%
012
33.3%
18
22.2%
32
 
5.6%
21
 
2.8%
41
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)36
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
.12
33.3%
012
33.3%
18
22.2%
32
 
5.6%
21
 
2.8%
41
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)36
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
.12
33.3%
012
33.3%
18
22.2%
32
 
5.6%
21
 
2.8%
41
 
2.8%

Operator Employee Fatalities
Categorical

Constant  Missing 

Distinct1
Distinct (%)12.5%
Missing2787
Missing (%)99.7%
Memory size152.9 KiB
0.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.08
 
0.3%
(Missing)2787
99.7%

Length

2026-01-20T00:33:22.379796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-20T00:33:22.408100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.08
100.0%

Most occurring characters

ValueCountFrequency (%)
016
66.7%
.8
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
016
66.7%
.8
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
016
66.7%
.8
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
016
66.7%
.8
33.3%

Operator Contractor Fatalities
Categorical

High correlation  Missing 

Distinct3
Distinct (%)37.5%
Missing2787
Missing (%)99.7%
Memory size152.9 KiB
0.0
1.0
2.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)12.5%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.05
 
0.2%
1.02
 
0.1%
2.01
 
< 0.1%
(Missing)2787
99.7%

Length

2026-01-20T00:33:22.443549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-20T00:33:22.479219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.05
62.5%
1.02
 
25.0%
2.01
 
12.5%

Most occurring characters

ValueCountFrequency (%)
013
54.2%
.8
33.3%
12
 
8.3%
21
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
013
54.2%
.8
33.3%
12
 
8.3%
21
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
013
54.2%
.8
33.3%
12
 
8.3%
21
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
013
54.2%
.8
33.3%
12
 
8.3%
21
 
4.2%

Emergency Responder Fatalities
Categorical

Constant  Missing 

Distinct1
Distinct (%)12.5%
Missing2787
Missing (%)99.7%
Memory size152.9 KiB
0.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.08
 
0.3%
(Missing)2787
99.7%

Length

2026-01-20T00:33:22.528299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-20T00:33:22.556425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.08
100.0%

Most occurring characters

ValueCountFrequency (%)
016
66.7%
.8
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
016
66.7%
.8
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
016
66.7%
.8
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
016
66.7%
.8
33.3%

Other Fatalities
Categorical

High correlation  Missing 

Distinct2
Distinct (%)25.0%
Missing2787
Missing (%)99.7%
Memory size152.9 KiB
0.0
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)12.5%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.07
 
0.3%
1.01
 
< 0.1%
(Missing)2787
99.7%

Length

2026-01-20T00:33:22.591937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-20T00:33:22.624321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.07
87.5%
1.01
 
12.5%

Most occurring characters

ValueCountFrequency (%)
015
62.5%
.8
33.3%
11
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
015
62.5%
.8
33.3%
11
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
015
62.5%
.8
33.3%
11
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
015
62.5%
.8
33.3%
11
 
4.2%

Public Fatalities
Categorical

High correlation  Missing 

Distinct3
Distinct (%)37.5%
Missing2787
Missing (%)99.7%
Memory size152.9 KiB
0.0
1.0
2.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)12.5%

Sample

1st row1.0
2nd row0.0
3rd row2.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.04
 
0.1%
1.03
 
0.1%
2.01
 
< 0.1%
(Missing)2787
99.7%

Length

2026-01-20T00:33:22.665136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-20T00:33:22.699788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.04
50.0%
1.03
37.5%
2.01
 
12.5%

Most occurring characters

ValueCountFrequency (%)
012
50.0%
.8
33.3%
13
 
12.5%
21
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
012
50.0%
.8
33.3%
13
 
12.5%
21
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
012
50.0%
.8
33.3%
13
 
12.5%
21
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
012
50.0%
.8
33.3%
13
 
12.5%
21
 
4.2%

All Fatalities
Categorical

High correlation  Missing 

Distinct2
Distinct (%)25.0%
Missing2787
Missing (%)99.7%
Memory size152.9 KiB
1.0
2.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row2.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.06
 
0.2%
2.02
 
0.1%
(Missing)2787
99.7%

Length

2026-01-20T00:33:22.742759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-20T00:33:22.773580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.06
75.0%
2.02
 
25.0%

Most occurring characters

ValueCountFrequency (%)
.8
33.3%
08
33.3%
16
25.0%
22
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
.8
33.3%
08
33.3%
16
25.0%
22
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
.8
33.3%
08
33.3%
16
25.0%
22
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
.8
33.3%
08
33.3%
16
25.0%
22
 
8.3%

Property Damage Costs
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct768
Distinct (%)27.5%
Missing7
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean112296.27
Minimum0
Maximum27000000
Zeros539
Zeros (%)19.3%
Negative0
Negative (%)0.0%
Memory size22.0 KiB
2026-01-20T00:33:22.823975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1100
median3000
Q325000
95-th percentile350000
Maximum27000000
Range27000000
Interquartile range (IQR)24900

Descriptive statistics

Standard deviation870994.74
Coefficient of variation (CV)7.7562217
Kurtosis584.38851
Mean112296.27
Median Absolute Deviation (MAD)3000
Skewness21.928212
Sum3.1308199 × 108
Variance7.5863184 × 1011
MonotonicityNot monotonic
2026-01-20T00:33:22.890385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0539
 
19.3%
1000118
 
4.2%
500101
 
3.6%
500088
 
3.1%
10083
 
3.0%
1000071
 
2.5%
200053
 
1.9%
150049
 
1.8%
250048
 
1.7%
2000047
 
1.7%
Other values (758)1591
56.9%
ValueCountFrequency (%)
0539
19.3%
11
 
< 0.1%
31
 
< 0.1%
54
 
0.1%
1012
 
0.4%
155
 
0.2%
209
 
0.3%
2514
 
0.5%
281
 
< 0.1%
304
 
0.1%
ValueCountFrequency (%)
270000001
< 0.1%
243234831
< 0.1%
141300001
< 0.1%
116140001
< 0.1%
98681731
< 0.1%
77013611
< 0.1%
73684701
< 0.1%
52770001
< 0.1%
50000001
< 0.1%
45885741
< 0.1%

Lost Commodity Costs
Real number (ℝ)

High correlation  Zeros 

Distinct748
Distinct (%)26.8%
Missing4
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean9805.2988
Minimum0
Maximum1417839
Zeros466
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size22.0 KiB
2026-01-20T00:33:22.950652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q120
median100
Q3700
95-th percentile31806.5
Maximum1417839
Range1417839
Interquartile range (IQR)680

Descriptive statistics

Standard deviation63840.75
Coefficient of variation (CV)6.5108418
Kurtosis204.19898
Mean9805.2988
Median Absolute Deviation (MAD)100
Skewness12.934035
Sum27366589
Variance4.0756413 × 109
MonotonicityNot monotonic
2026-01-20T00:33:23.014884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0466
 
16.7%
100118
 
4.2%
50117
 
4.2%
20070
 
2.5%
2565
 
2.3%
2060
 
2.1%
50056
 
2.0%
15055
 
2.0%
30049
 
1.8%
3049
 
1.8%
Other values (738)1686
60.3%
ValueCountFrequency (%)
0466
16.7%
118
 
0.6%
214
 
0.5%
39
 
0.3%
43
 
0.1%
512
 
0.4%
64
 
0.1%
74
 
0.1%
85
 
0.2%
96
 
0.2%
ValueCountFrequency (%)
14178391
< 0.1%
10924481
< 0.1%
10010201
< 0.1%
9957281
< 0.1%
9779371
< 0.1%
7733691
< 0.1%
7340721
< 0.1%
7061401
< 0.1%
6880001
< 0.1%
6229341
< 0.1%

Public/Private Property Damage Costs
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct110
Distinct (%)3.9%
Missing10
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean25121.949
Minimum0
Maximum23000000
Zeros2527
Zeros (%)90.4%
Negative0
Negative (%)0.0%
Memory size22.0 KiB
2026-01-20T00:33:23.085685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5000
Maximum23000000
Range23000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation524358.08
Coefficient of variation (CV)20.872508
Kurtosis1444.8177
Mean25121.949
Median Absolute Deviation (MAD)0
Skewness35.946083
Sum69964627
Variance2.749514 × 1011
MonotonicityNot monotonic
2026-01-20T00:33:23.153974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02527
90.4%
500026
 
0.9%
100022
 
0.8%
50014
 
0.5%
1000013
 
0.5%
200012
 
0.4%
250011
 
0.4%
2500010
 
0.4%
200007
 
0.3%
500006
 
0.2%
Other values (100)137
 
4.9%
(Missing)10
 
0.4%
ValueCountFrequency (%)
02527
90.4%
11
 
< 0.1%
251
 
< 0.1%
501
 
< 0.1%
1001
 
< 0.1%
1501
 
< 0.1%
2502
 
0.1%
3001
 
< 0.1%
3501
 
< 0.1%
4001
 
< 0.1%
ValueCountFrequency (%)
230000001
< 0.1%
122337871
< 0.1%
75000001
< 0.1%
24210001
< 0.1%
22926851
< 0.1%
20000001
< 0.1%
17400001
< 0.1%
16344971
< 0.1%
13904801
< 0.1%
12732611
< 0.1%

Emergency Response Costs
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct712
Distinct (%)25.5%
Missing6
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean291891.13
Minimum0
Maximum1.77 × 108
Zeros809
Zeros (%)28.9%
Negative0
Negative (%)0.0%
Memory size22.0 KiB
2026-01-20T00:33:23.222507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2520
Q315500
95-th percentile278424.8
Maximum1.77 × 108
Range1.77 × 108
Interquartile range (IQR)15500

Descriptive statistics

Standard deviation4617076.5
Coefficient of variation (CV)15.817803
Kurtosis926.12377
Mean291891.13
Median Absolute Deviation (MAD)2520
Skewness28.418607
Sum8.1408437 × 108
Variance2.1317395 × 1013
MonotonicityNot monotonic
2026-01-20T00:33:23.298410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0809
28.9%
5000125
 
4.5%
100088
 
3.1%
200078
 
2.8%
1000077
 
2.8%
300063
 
2.3%
50062
 
2.2%
150045
 
1.6%
2000044
 
1.6%
250044
 
1.6%
Other values (702)1354
48.4%
ValueCountFrequency (%)
0809
28.9%
302
 
0.1%
401
 
< 0.1%
508
 
0.3%
601
 
< 0.1%
651
 
< 0.1%
751
 
< 0.1%
10022
 
0.8%
1204
 
0.1%
1253
 
0.1%
ValueCountFrequency (%)
1770000001
< 0.1%
1000000001
< 0.1%
907010421
< 0.1%
647000001
< 0.1%
640000001
< 0.1%
184234991
< 0.1%
148859031
< 0.1%
140915541
< 0.1%
121462321
< 0.1%
100586981
< 0.1%

Environmental Remediation Costs
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct628
Distinct (%)22.5%
Missing8
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean362809.44
Minimum0
Maximum6.35 × 108
Zeros1240
Zeros (%)44.4%
Negative0
Negative (%)0.0%
Memory size22.0 KiB
2026-01-20T00:33:23.364614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median800
Q315000
95-th percentile331250
Maximum6.35 × 108
Range6.35 × 108
Interquartile range (IQR)15000

Descriptive statistics

Standard deviation12068872
Coefficient of variation (CV)33.265044
Kurtosis2747.3699
Mean362809.44
Median Absolute Deviation (MAD)800
Skewness52.237171
Sum1.0111499 × 109
Variance1.4565767 × 1014
MonotonicityNot monotonic
2026-01-20T00:33:23.437528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01240
44.4%
500094
 
3.4%
100073
 
2.6%
1000054
 
1.9%
50051
 
1.8%
200045
 
1.6%
1500041
 
1.5%
300037
 
1.3%
2000036
 
1.3%
2500033
 
1.2%
Other values (618)1083
38.7%
ValueCountFrequency (%)
01240
44.4%
351
 
< 0.1%
503
 
0.1%
901
 
< 0.1%
10017
 
0.6%
1251
 
< 0.1%
1501
 
< 0.1%
1701
 
< 0.1%
1851
 
< 0.1%
20019
 
0.7%
ValueCountFrequency (%)
6350000001
< 0.1%
224219331
< 0.1%
203000001
< 0.1%
201000001
< 0.1%
180367891
< 0.1%
165389631
< 0.1%
136566871
< 0.1%
110318001
< 0.1%
101523651
< 0.1%
74602741
< 0.1%

Other Costs
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct175
Distinct (%)6.3%
Missing16
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean34356.04
Minimum0
Maximum22350000
Zeros2465
Zeros (%)88.2%
Negative0
Negative (%)0.0%
Memory size22.0 KiB
2026-01-20T00:33:23.506480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile12420.2
Maximum22350000
Range22350000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation619123.48
Coefficient of variation (CV)18.020805
Kurtosis991.51925
Mean34356.04
Median Absolute Deviation (MAD)0
Skewness29.872262
Sum95475434
Variance3.8331388 × 1011
MonotonicityNot monotonic
2026-01-20T00:33:23.573891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02465
88.2%
500013
 
0.5%
1000012
 
0.4%
50012
 
0.4%
150010
 
0.4%
100010
 
0.4%
150009
 
0.3%
20009
 
0.3%
40008
 
0.3%
500008
 
0.3%
Other values (165)223
 
8.0%
(Missing)16
 
0.6%
ValueCountFrequency (%)
02465
88.2%
501
 
< 0.1%
701
 
< 0.1%
1001
 
< 0.1%
1501
 
< 0.1%
2002
 
0.1%
2241
 
< 0.1%
2251
 
< 0.1%
2491
 
< 0.1%
2502
 
0.1%
ValueCountFrequency (%)
223500001
< 0.1%
197967361
< 0.1%
64225271
< 0.1%
58097331
< 0.1%
53000001
< 0.1%
48000001
< 0.1%
40000001
< 0.1%
35942601
< 0.1%
24000001
< 0.1%
20000002
0.1%

All Costs
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct2279
Distinct (%)81.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean834033.25
Minimum0
Maximum8.4052612 × 108
Zeros34
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size22.0 KiB
2026-01-20T00:33:23.638267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile396.1
Q15039.5
median23129
Q3117232.5
95-th percentile1278268
Maximum8.4052612 × 108
Range8.4052612 × 108
Interquartile range (IQR)112193

Descriptive statistics

Standard deviation16578298
Coefficient of variation (CV)19.877263
Kurtosis2361.5433
Mean834033.25
Median Absolute Deviation (MAD)21529
Skewness47.05731
Sum2.3311229 × 109
Variance2.7483998 × 1014
MonotonicityNot monotonic
2026-01-20T00:33:23.706308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
034
 
1.2%
500021
 
0.8%
100011
 
0.4%
2000010
 
0.4%
3000010
 
0.4%
1000010
 
0.4%
20009
 
0.3%
150009
 
0.3%
60007
 
0.3%
10507
 
0.3%
Other values (2269)2667
95.4%
ValueCountFrequency (%)
034
1.2%
12
 
0.1%
21
 
< 0.1%
31
 
< 0.1%
91
 
< 0.1%
102
 
0.1%
141
 
< 0.1%
181
 
< 0.1%
201
 
< 0.1%
231
 
< 0.1%
ValueCountFrequency (%)
8405261181
< 0.1%
1429318841
< 0.1%
1350000001
< 0.1%
913000001
< 0.1%
662340721
< 0.1%
473935661
< 0.1%
357289031
< 0.1%
322337401
< 0.1%
289380001
< 0.1%
243234831
< 0.1%

Interactions

2026-01-20T00:33:15.967790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:32:58.631674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:32:59.679625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:00.717461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:01.745361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:02.796118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:03.748276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:04.704390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:05.710487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:06.762092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:07.791687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:08.889896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:09.896451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:10.881940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:11.912961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:12.878103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:13.915810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:14.917197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:16.026325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:32:58.698284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:32:59.737632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:00.774319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:01.804280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:02.850838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:03.801959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:04.760674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:05.769199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:06.820297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:07.847993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:08.946175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:09.949865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:10.940843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:11.967798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:12.936530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:13.972831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:14.968817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:16.076448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:32:58.757221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:32:59.793316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:00.835401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:01.857058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:02.915300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:03.860849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:04.822901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:05.831927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:06.883758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:07.904753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:09.000875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:10.008017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:10.998008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:12.022493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:12.993847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:14.029468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:15.023722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:16.129896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:32:58.814767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:32:59.855417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:00.888867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:01.910350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:02.966333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:03.913399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:04.879061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:05.887632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:06.939914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:07.965940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:09.059409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:10.062352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:11.061140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:12.077208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:13.053831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:14.083561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:15.074979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:16.181825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:32:58.872116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:32:59.908673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:00.942015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:01.959111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:03.015838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:03.964051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:04.931589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:05.940898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:06.993494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:08.018725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:09.114076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:10.117037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:11.119817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:12.137302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:13.109081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:14.140042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:15.126431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:16.234954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:32:58.925550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:32:59.963998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:00.997676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:02.008676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:03.067964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:04.014853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:04.983341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:05.994277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:07.047450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:08.070404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:09.171277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:10.173604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:11.178578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:12.191489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:13.175157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:14.197179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:15.181204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:16.283186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:32:58.979760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:00.023312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:01.057864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:02.063124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:03.121683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:04.067755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:05.037882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:06.048220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:07.106032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:08.128720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:09.226374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:10.223269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:11.232546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:12.242676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:13.231544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:14.257303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:15.232895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:16.333654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:32:59.042426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:00.086412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:01.118635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:02.126129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:03.175508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:04.124203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:05.097823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:06.107340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:07.166988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:08.183309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:09.281849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:10.277650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:11.288865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:12.294887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:13.293964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:14.310463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:15.403867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:16.393484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:32:59.104464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:00.156557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:01.182628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:02.179517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:03.232622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:04.185457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:05.160136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:06.169124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:07.234991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:08.238177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:09.343642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:10.333344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:11.344162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:12.351500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:13.349020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:14.372993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:15.460460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:16.445801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:32:59.166093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:00.214292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:01.240381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:02.232991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:03.285050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:04.238222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:05.214946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:06.243339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:07.288602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:08.298195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:09.399605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:10.392325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:11.406591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:12.408542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:13.406098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:14.428827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:15.513805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:16.495595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:32:59.221423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:00.270179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:01.296533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:02.282900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:03.337031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:04.292733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:05.268805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:06.299169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:07.344152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:08.350400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:09.453873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:10.445631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:11.461459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:12.466535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:13.462104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:14.482446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:15.564068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:16.545820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:32:59.278150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:00.327359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:01.355082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:02.335398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:03.388545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:04.344696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:05.324636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:06.359504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:07.399291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:08.405394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:09.506652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:10.499218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:11.518170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:12.518390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:13.526115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:14.536437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:15.616920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:16.598799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:32:59.333395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:00.381596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:01.411854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:02.387664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:03.438552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:04.395832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:05.379075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:06.416626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:07.453893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:08.457194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:09.560369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:10.552735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:11.576024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:12.567996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:13.580240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:14.595206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:15.664477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:16.660573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:32:59.391178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:00.440278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:01.475322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:02.439134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:03.491900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:04.448426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:05.435666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:06.472724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:07.511204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:08.513400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:09.617533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:10.609525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:11.635169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:12.622237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:13.639342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:14.651150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:15.720099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:16.715111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:32:59.444715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:00.499913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:01.529609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:02.593265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:03.546989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:04.504259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:05.494817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:06.530483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:07.572608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:08.566900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:09.674010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:10.662634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:11.689526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:12.670397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:13.694485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:14.703686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:15.767787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:16.768286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:32:59.509424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:00.559032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:01.587133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:02.648362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:03.602238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:04.558033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:05.551885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:06.593036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:07.631862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:08.631489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:09.731705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:10.726114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:11.754353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:12.725637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:13.750362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:14.761036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:15.822916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:16.817238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:32:59.574652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:00.613495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:01.642367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:02.700417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:03.654375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:04.609906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:05.606204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:06.653200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:07.688253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:08.788672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:09.785720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:10.780883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:11.809889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:12.777142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:13.806361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:14.812921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:15.874942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:16.863888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:32:59.627425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:00.665404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:01.694690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:02.748610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:03.700763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:04.658486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:05.658645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:06.706769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:07.739982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:08.839815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:09.836996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:10.831819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:11.862049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:12.829549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:13.863878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:14.862974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T00:33:15.921455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-01-20T00:33:23.786086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Accident LatitudeAccident LongitudeAccident StateAccident YearAll CostsAll FatalitiesAll InjuriesCause CategoryCause SubcategoryEmergency Response CostsEnvironmental Remediation CostsIntentional Release (Barrels)Liquid ExplosionLiquid IgnitionLiquid Recovery (Barrels)Liquid SubtypeLiquid TypeLost Commodity CostsNet Loss (Barrels)Operator Contractor FatalitiesOperator Contractor InjuriesOperator Employee InjuriesOperator IDOther CostsOther FatalitiesPipeline LocationPipeline ShutdownPipeline TypeProperty Damage CostsPublic EvacuationsPublic FatalitiesPublic InjuriesPublic/Private Property Damage CostsReport NumberSupplemental NumberUnintentional Release (Barrels)
Accident Latitude1.0000.0530.720-0.0540.0490.0000.0000.0660.1060.1200.158-0.0130.0090.0300.0790.1470.142-0.067-0.0610.0000.0000.000-0.0570.0580.0000.2450.0100.034-0.0100.0700.5610.4640.094-0.055-0.004-0.008
Accident Longitude0.0531.0000.714-0.0620.1111.0001.0000.0240.0570.0950.012-0.0610.0000.043-0.0510.1310.182-0.143-0.1031.0001.0001.000-0.205-0.0141.0000.0000.0450.0710.0500.0761.0001.000-0.048-0.081-0.058-0.151
Accident State0.7200.7141.0000.0520.1410.5770.0000.1180.0800.2090.1340.1420.1670.0950.0580.2270.2610.1690.3400.2710.0000.0000.3250.2090.5770.1310.1140.1270.1450.0000.3570.7450.0650.0440.0860.294
Accident Year-0.054-0.0620.0521.000-0.0050.0000.0000.0590.0940.006-0.0950.2220.0000.0000.0520.0400.040-0.037-0.0970.0000.0000.6120.107-0.0580.5770.0000.0420.0660.022-0.0730.0000.051-0.0130.9830.860-0.016
All Costs0.0490.1110.141-0.0051.0001.0001.0000.0000.1650.5930.5120.0000.0000.0000.3851.0000.0000.4280.1821.0001.0001.000-0.0610.1851.0000.0000.0230.0150.6700.1921.0001.0000.312-0.0140.0790.461
All Fatalities0.0001.0000.5770.0001.0001.0000.8660.0000.0001.0001.0001.0000.0000.0001.0000.7070.6670.0000.0000.4590.2170.0000.0001.0000.0001.0000.0000.0000.0001.0000.5000.3541.0000.0000.0000.000
All Injuries0.0001.0000.0000.0001.0000.8661.0000.0000.0001.0001.0001.0000.0000.0001.0000.0000.3071.0000.4720.5530.7980.0000.0001.0001.0001.0000.0000.0001.0001.0000.5530.0001.0000.0000.0000.472
Cause Category0.0660.0240.1180.0590.0000.0000.0001.0000.9940.0660.0000.0540.0070.1780.0440.0510.1180.0820.0770.0820.0000.0000.0470.0440.0000.0530.1550.3040.0640.0880.0000.7050.0660.0700.0880.079
Cause Subcategory0.1060.0570.0800.0940.1650.0000.0000.9941.0000.1870.2210.0840.2160.4040.1440.1820.2130.3680.3640.0000.0000.4570.0690.1220.7070.1220.2240.4100.1830.1800.0000.6460.2110.1130.1130.367
Emergency Response Costs0.1200.0950.2090.0060.5931.0001.0000.0660.1871.0000.327-0.1210.0000.0000.4350.0000.0000.2910.0401.0001.0001.000-0.1780.1131.0000.0000.0210.0000.2590.1171.0001.0000.242-0.0010.0620.385
Environmental Remediation Costs0.1580.0120.134-0.0950.5121.0001.0000.0000.2210.3271.000-0.2720.0000.0000.4501.0000.0000.3340.1111.0001.0001.000-0.0740.0881.0000.0000.0000.0000.1860.0381.0001.0000.262-0.100-0.0290.382
Intentional Release (Barrels)-0.013-0.0610.1420.2220.0001.0001.0000.0540.084-0.121-0.2721.0000.0000.000-0.3470.0000.1680.2080.2831.0001.0001.0000.262-0.0011.0000.0000.0000.0000.1380.0481.0001.0000.0570.2110.1520.075
Liquid Explosion0.0090.0000.1670.0000.0000.0000.0000.0070.2160.0000.0000.0001.0000.3780.0000.1320.1140.2050.4750.0000.0000.0000.0080.1210.0000.0000.0150.0000.0000.2540.0000.0000.0000.0000.0380.398
Liquid Ignition0.0300.0430.0950.0000.0000.0000.0000.1780.4040.0000.0000.0000.3781.0000.0000.1770.1330.1260.2270.0000.0000.0000.0810.0600.0000.0000.0000.0000.0000.1390.0000.0000.0000.0410.0740.193
Liquid Recovery (Barrels)0.079-0.0510.0580.0520.3851.0001.0000.0440.1440.4350.450-0.3470.0000.0001.0000.0000.0000.347-0.1921.0001.0001.000-0.1700.0831.0000.0000.0470.0310.119-0.0331.0001.0000.1220.0470.0990.604
Liquid Subtype0.1470.1310.2270.0401.0000.7070.0000.0510.1820.0001.0000.0000.1320.1770.0001.0000.9980.0320.0460.0000.0000.3650.3150.0000.0001.0000.1810.1780.0470.0000.0000.2001.0000.0290.0940.045
Liquid Type0.1420.1820.2610.0400.0000.6670.3070.1180.2130.0000.0000.1680.1140.1330.0000.9981.0000.0480.0760.6020.2920.2920.2070.0000.0000.0710.0860.0870.0000.0000.2690.2890.0000.0380.1110.053
Lost Commodity Costs-0.067-0.1430.169-0.0370.4280.0001.0000.0820.3680.2910.3340.2080.2050.1260.3470.0320.0481.0000.4240.0001.0001.0000.0480.0810.9130.0000.0660.0470.2360.1900.0001.0000.252-0.0360.0050.703
Net Loss (Barrels)-0.061-0.1030.340-0.0970.1820.0000.4720.0770.3640.0400.1110.2830.4750.227-0.1920.0460.0760.4241.0000.0000.3870.0000.1560.0910.9130.0000.0550.0520.1330.2080.0000.0000.197-0.093-0.0590.442
Operator Contractor Fatalities0.0001.0000.2710.0001.0000.4590.5530.0820.0001.0001.0001.0000.0000.0001.0000.0000.6020.0000.0001.0000.6920.0000.0001.0000.0001.0000.4080.3650.0001.0000.1410.4081.0000.0000.0000.000
Operator Contractor Injuries0.0001.0000.0000.0001.0000.2170.7980.0000.0001.0001.0001.0000.0000.0001.0000.0000.2921.0000.3870.6921.0000.0000.0001.0001.0001.0000.6280.0001.0001.0000.0000.0001.0000.0770.0000.387
Operator Employee Injuries0.0001.0000.0000.6121.0000.0000.0000.0000.4571.0001.0001.0000.0000.0001.0000.3650.2921.0000.0000.0000.0001.0000.0001.0001.0001.0000.0000.0001.0001.0000.0000.0001.0000.0000.0000.000
Operator ID-0.057-0.2050.3250.107-0.0610.0000.0000.0470.069-0.178-0.0740.2620.0080.081-0.1700.3150.2070.0480.1560.0000.0000.0001.000-0.0240.0000.0220.0040.085-0.007-0.0190.0000.0000.0020.1200.1080.003
Other Costs0.058-0.0140.209-0.0580.1851.0001.0000.0440.1220.1130.088-0.0010.1210.0600.0830.0000.0000.0810.0911.0001.0001.000-0.0241.0001.0000.0000.0380.0010.0520.1371.0001.0000.151-0.059-0.0030.116
Other Fatalities0.0001.0000.5770.5771.0000.0001.0000.0000.7071.0001.0001.0000.0000.0001.0000.0000.0000.9130.9130.0001.0001.0000.0001.0001.0001.0000.0000.0000.2181.0000.0001.0001.0000.5770.4080.913
Pipeline Location0.2450.0000.1310.0000.0001.0001.0000.0530.1220.0000.0000.0000.0000.0000.0001.0000.0710.0000.0001.0001.0001.0000.0220.0001.0001.0000.0001.0000.0000.0001.0001.0000.0000.0000.0760.000
Pipeline Shutdown0.0100.0450.1140.0420.0230.0000.0000.1550.2240.0210.0000.0000.0150.0000.0470.1810.0860.0660.0550.4080.6280.0000.0040.0380.0000.0001.0000.2600.0240.0040.0000.4080.0000.0340.1420.077
Pipeline Type0.0340.0710.1270.0660.0150.0000.0000.3040.4100.0000.0000.0000.0000.0000.0310.1780.0870.0470.0520.3650.0000.0000.0850.0010.0001.0000.2601.0000.0170.0000.0000.2490.0000.0650.1430.070
Property Damage Costs-0.0100.0500.1450.0220.6700.0001.0000.0640.1830.2590.1860.1380.0000.0000.1190.0470.0000.2360.1330.0001.0001.000-0.0070.0520.2180.0000.0240.0171.0000.1770.0001.0000.2040.0210.0770.221
Public Evacuations0.0700.0760.000-0.0730.1921.0001.0000.0880.1800.1170.0380.0480.2540.139-0.0330.0000.0000.1900.2081.0001.0001.000-0.0190.1371.0000.0000.0040.0000.1771.0001.0001.0000.185-0.073-0.0190.189
Public Fatalities0.5611.0000.3570.0001.0000.5000.5530.0000.0001.0001.0001.0000.0000.0001.0000.0000.2690.0000.0000.1410.0000.0000.0001.0000.0001.0000.0000.0000.0001.0001.0001.0001.0000.0000.0000.000
Public Injuries0.4641.0000.7450.0511.0000.3540.0000.7050.6461.0001.0001.0000.0000.0001.0000.2000.2891.0000.0000.4080.0000.0000.0001.0001.0001.0000.4080.2491.0001.0001.0001.0001.0000.0000.4420.000
Public/Private Property Damage Costs0.094-0.0480.065-0.0130.3121.0001.0000.0660.2110.2420.2620.0570.0000.0000.1221.0000.0000.2520.1971.0001.0001.0000.0020.1511.0000.0000.0000.0000.2040.1851.0001.0001.000-0.0170.0270.241
Report Number-0.055-0.0810.0440.983-0.0140.0000.0000.0700.113-0.001-0.1000.2110.0000.0410.0470.0290.038-0.036-0.0930.0000.0770.0000.120-0.0590.5770.0000.0340.0650.021-0.0730.0000.000-0.0171.0000.876-0.015
Supplemental Number-0.004-0.0580.0860.8600.0790.0000.0000.0880.1130.062-0.0290.1520.0380.0740.0990.0940.1110.005-0.0590.0000.0000.0000.108-0.0030.4080.0760.1420.1430.077-0.0190.0000.4420.0270.8761.0000.040
Unintentional Release (Barrels)-0.008-0.1510.294-0.0160.4610.0000.4720.0790.3670.3850.3820.0750.3980.1930.6040.0450.0530.7030.4420.0000.3870.0000.0030.1160.9130.0000.0770.0700.2210.1890.0000.0000.241-0.0150.0401.000

Missing values

2026-01-20T00:33:16.989033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-20T00:33:17.206083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-01-20T00:33:17.509154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Report NumberSupplemental NumberAccident YearAccident Date/TimeOperator IDOperator NamePipeline/Facility NamePipeline LocationPipeline TypeLiquid TypeLiquid SubtypeLiquid NameAccident CityAccident CountyAccident StateAccident LatitudeAccident LongitudeCause CategoryCause SubcategoryUnintentional Release (Barrels)Intentional Release (Barrels)Liquid Recovery (Barrels)Net Loss (Barrels)Liquid IgnitionLiquid ExplosionPipeline ShutdownShutdown Date/TimeRestart Date/TimePublic EvacuationsOperator Employee InjuriesOperator Contractor InjuriesEmergency Responder InjuriesOther InjuriesPublic InjuriesAll InjuriesOperator Employee FatalitiesOperator Contractor FatalitiesEmergency Responder FatalitiesOther FatalitiesPublic FatalitiesAll FatalitiesProperty Damage CostsLost Commodity CostsPublic/Private Property Damage CostsEmergency Response CostsEnvironmental Remediation CostsOther CostsAll Costs
0201000161730520101/1/2010 7:15 AM32109ONEOK NGL PIPELINE LPKINDER MORGAN JCTONSHOREABOVEGROUNDHVL OR OTHER FLAMMABLE OR TOXIC FLUID, GASLPG (LIQUEFIED PETROLEUM GAS) / NGL (NATURAL GAS LIQUID)NaNMCPHERSONMCPHERSONKS38.67070-97.78123INCORRECT OPERATIONPIPELINE/EQUIPMENT OVERPRESSURED21.000.10.0021.00NONONONaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN110.01517.00.00.00.00.01627
1201002541733120101/4/2010 8:30 AM15786PORTLAND PIPELINE CORP24-INCH MAIN LINEONSHOREABOVEGROUNDCRUDE OILNaNNaNRAYMONDCUMBERLANDME43.94028-70.49336MATERIAL/WELD/EQUIP FAILUREPUMP OR PUMP-RELATED EQUIPMENT0.120.00.120.00NONONaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4000.08.00.00.00.00.04008
2201000381774720101/5/2010 10:30 AM20160PETROLOGISTICS OLEFINS, LLCNaNONSHOREABOVEGROUNDHVL OR OTHER FLAMMABLE OR TOXIC FLUID, GASOTHER HVLETHANESULPHERCALCASIEULA30.18240-93.35240MATERIAL/WELD/EQUIP FAILUREDEFECTIVE OR LOOSE TUBING/FITTING2.000.00.002.00NONONaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0200.00.00.00.00.0200
3201002601857420101/6/2010 7:30 PM11169ENBRIDGE ENERGY, LIMITED PARTNERSHIPSUPERIOR TERMINALONSHOREUNDERGROUNDCRUDE OILNaNNaNSUPERIORDOUGLASWI46.68930-92.06120NATURAL FORCE DAMAGETEMPERATURE0.480.00.480.00NONONaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN200.040.00.011300.00.00.011540
4201000301627620101/7/2010 1:00 PM300PLAINS PIPELINE, L.P.RED RIVER EASTONSHOREUNDERGROUNDCRUDE OILNaNNaNSHERMANGRAYSONTX33.58266-96.64881EXCAVATION DAMAGETHIRD PARTY EXCAVATION DAMAGE700.00NaN698.002.00NONONONaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN20000.0150.00.07500.02000.00.029650
5201000211716120101/8/2010 11:38 PM11169ENBRIDGE ENERGY, LIMITED PARTNERSHIPNaNONSHOREUNDERGROUNDCRUDE OILNaNNaNNECHEPEMBINAND48.99555-97.52554MATERIAL/WELD/EQUIP FAILUREMANUFACTURING-RELATED3784.000.01547.002237.00NONOYES1/8/2010 23:411/13/2010 9:170.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN76940.0167775.0150000.01800000.02000000.00.04194715
6201100361805220101/9/2010 12:15 AM26041KINDER MORGAN LIQUID TERMINALS, LLCNaNONSHORETANKREFINED AND/OR PETROLEUM PRODUCT (NON-HVL), LIQUIDGASOLINE (NON-ETHANOL)NaNGALENA PARKHARRISTX29.43050-95.12010MATERIAL/WELD/EQUIP FAILUREENVIRONMENTAL CRACKING-RELATED35.000.030.005.00NONONONaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0400.00.00.070000.00.070400
7201002551858420101/9/2010 1:12 AM12624MOBIL CORPHULL STATIONONSHOREABOVEGROUNDHVL OR OTHER FLAMMABLE OR TOXIC FLUID, GASLPG (LIQUEFIED PETROLEUM GAS) / NGL (NATURAL GAS LIQUID)NaNHULLLIBERTYTX30.08533-94.38050NATURAL FORCE DAMAGETEMPERATURE0.240.00.000.24NONONaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN400.013.00.00.00.00.0413
8201002611805020101/10/2010 7:46 PM26041KINDER MORGAN LIQUID TERMINALS, LLCNaNONSHOREABOVEGROUNDREFINED AND/OR PETROLEUM PRODUCT (NON-HVL), LIQUIDDIESEL, FUEL OIL, KEROSENE, JET FUELNaNNaNNaNTX29.43050-95.12010MATERIAL/WELD/EQUIP FAILUREOTHER EQUIPMENT FAILURE0.400.00.400.00NONONaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0336.00.00.040000.00.040336
9201000241839020101/11/2010 2:30 PM31684CONOCOPHILLIPSTANK 1501ONSHORETANKREFINED AND/OR PETROLEUM PRODUCT (NON-HVL), LIQUIDGASOLINE (NON-ETHANOL)NaNPASADENAHARRISTX29.71478-95.17611ALL OTHER CAUSESMISCELLANEOUS0.480.00.480.00NONONONaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.050.00.010000.010000.010000.030050
Report NumberSupplemental NumberAccident YearAccident Date/TimeOperator IDOperator NamePipeline/Facility NamePipeline LocationPipeline TypeLiquid TypeLiquid SubtypeLiquid NameAccident CityAccident CountyAccident StateAccident LatitudeAccident LongitudeCause CategoryCause SubcategoryUnintentional Release (Barrels)Intentional Release (Barrels)Liquid Recovery (Barrels)Net Loss (Barrels)Liquid IgnitionLiquid ExplosionPipeline ShutdownShutdown Date/TimeRestart Date/TimePublic EvacuationsOperator Employee InjuriesOperator Contractor InjuriesEmergency Responder InjuriesOther InjuriesPublic InjuriesAll InjuriesOperator Employee FatalitiesOperator Contractor FatalitiesEmergency Responder FatalitiesOther FatalitiesPublic FatalitiesAll FatalitiesProperty Damage CostsLost Commodity CostsPublic/Private Property Damage CostsEmergency Response CostsEnvironmental Remediation CostsOther CostsAll Costs
27852017002022026201612/21/2016 11:40 AM32147MARATHON PIPE LINE LLCELWOOD STATIONONSHOREABOVEGROUNDCRUDE OILNaNNaNELWOODTIPTON COUNTYIN40.291648-85.862792MATERIAL/WELD/EQUIP FAILUREPUMP OR PUMP-RELATED EQUIPMENT1.70NaN1.700.0NONOYES12/21/2016 11:4012/22/2016 6:100.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN48264.0103.00.017674.00.00.066041
27862017002222029201612/22/2016 9:25 PM32080CCPS TRANSPORTATION, LLCLINE 59 KEY STATIONONSHOREABOVEGROUNDCRUDE OILNaNNaNSALISBURYCHARITONMO39.414038-92.840164MATERIAL/WELD/EQUIP FAILUREPUMP OR PUMP-RELATED EQUIPMENT12.50NaN12.500.0NONOYES12/22/2016 21:5312/23/2016 2:260.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN15020.027.00.032000.00.00.047047
27872017002122027201612/23/2016 3:00 PM30829ENTERPRISE CRUDE PIPELINE LLCECHO MANIFOLDONSHOREABOVEGROUNDCRUDE OILNaNNaNHOUSTONHARRISTX29.614125-95.184793ALL OTHER CAUSESUNKNOWN3.00NaN3.000.0NONONONaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN65000.075.00.00.00.00.065075
27882017002322030201612/24/2016 9:35 AM31684PHILLIPS 66 PIPELINE LLCNT-90, PREDO TO CARSONONSHOREABOVEGROUNDCRUDE OILNaNNaNNaNARCHERTX33.655692-98.624766MATERIAL/WELD/EQUIP FAILURENON-THREADED CONNECTION FAILURE202.00NaN202.000.0NONONONaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4100.010600.050000.0105000.087810.01000.0258510
27892017002622044201612/26/2016 7:20 AM32011HOLLY ENERGY PARTNERS - OPERATING, L.P.RUSSELL STATIONONSHOREABOVEGROUNDCRUDE OILNaNNaNNaNGAINESTX32.861800-102.919210CORROSIONINTERNAL3.00NaN2.001.0NONOYES12/26/2016 7:2012/26/2016 10:000.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1000.0120.00.02000.010000.00.013120
27902017001522020201612/27/2016 8:40 AM32334TC OIL PIPELINE OPERATIONS INCKEYSTONEONSHOREABOVEGROUNDCRUDE OILNaNNaNTINACARROLLMO39.517036-93.486055MATERIAL/WELD/EQUIP FAILURETHREADED CONNECTION/COUPLING FAILURE0.25NaN0.250.0NONOYES12/27/2016 8:5412/27/2016 16:110.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.015.00.00.061000.00.061015
27912017002822046201612/28/2016 4:20 PM4906EXXONMOBIL PIPELINE COBRRF - CHOCTAW ETHANE/PROPANE MIX SYSTEMONSHOREUNDERGROUNDHVL OR OTHER FLAMMABLE OR TOXIC FLUID, GASOTHER HVL98.7% ETHANE, .97% METHANE, .36% PROPANENaNWEST BATON ROUGELA30.332597-91.274491ALL OTHER CAUSESUNKNOWN580.000.00.00580.0NONOYES12/28/2016 16:20NaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.05400.00.00.00.0100000.0105400
27922017002722045201612/29/2016 6:40 AM39145ENBRIDGE STORAGE (CUSHING) L.L.C.CUSHING CENTRAL TERMINALONSHORETANKCRUDE OILNaNNaNCUSHINGPAYNEOK35.565292-96.454956MATERIAL/WELD/EQUIP FAILUREOTHER EQUIPMENT FAILURE1.00NaN1.000.0NONONONaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN7000.050.00.05000.03000.00.015050
2793201700242203220171/3/2017 10:00 AM32147MARATHON PIPE LINE LLCMIDLAND STATIONONSHOREUNDERGROUNDREFINED AND/OR PETROLEUM PRODUCT (NON-HVL), LIQUIDMIXTURE OF REFINED PRODUCTS (TRANSMIX OR OTHER MIXTURE)NaNINDUSTRYBEAVER COUNTYPA40.631074-80.440463ALL OTHER CAUSESUNKNOWN0.20NaN0.200.0NONONONaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN11852.011.00.029565.00.00.041428
2794201700252204020171/9/2017 7:40 AM30829ENTERPRISE CRUDE PIPELINE LLCMARSHALL STATIONONSHOREABOVEGROUNDCRUDE OILNaNNaNGONZALESGONZALESTX29.305410-97.400301INCORRECT OPERATIONINCORRECT INSTALLATION4.00NaN4.000.0NONONONaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN45600.0200.00.00.00.00.045800